{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t_range = pd.date_range('2016-01-01', '2016-12-31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
       "               '2016-01-05', '2016-01-06', '2016-01-07', '2016-01-08',\n",
       "               '2016-01-09', '2016-01-10',\n",
       "               ...\n",
       "               '2016-12-22', '2016-12-23', '2016-12-24', '2016-12-25',\n",
       "               '2016-12-26', '2016-12-27', '2016-12-28', '2016-12-29',\n",
       "               '2016-12-30', '2016-12-31'],\n",
       "              dtype='datetime64[ns]', length=366, freq='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s1 = Series(np.random.randn(len(t_range)), index=t_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.13632025497138667"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1['2016-01'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s1_month = s1.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30',\n",
       "               '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31',\n",
       "               '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1_month.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01 00:00:00    0.566248\n",
       "2016-01-01 01:00:00   -0.062260\n",
       "2016-01-01 02:00:00   -0.062260\n",
       "2016-01-01 03:00:00   -0.062260\n",
       "2016-01-01 04:00:00   -0.062260\n",
       "2016-01-01 05:00:00   -0.062260\n",
       "2016-01-01 06:00:00   -0.062260\n",
       "2016-01-01 07:00:00   -0.062260\n",
       "2016-01-01 08:00:00   -0.062260\n",
       "2016-01-01 09:00:00   -0.062260\n",
       "2016-01-01 10:00:00   -0.062260\n",
       "2016-01-01 11:00:00   -0.062260\n",
       "2016-01-01 12:00:00   -0.062260\n",
       "2016-01-01 13:00:00   -0.062260\n",
       "2016-01-01 14:00:00   -0.062260\n",
       "2016-01-01 15:00:00   -0.062260\n",
       "2016-01-01 16:00:00   -0.062260\n",
       "2016-01-01 17:00:00   -0.062260\n",
       "2016-01-01 18:00:00   -0.062260\n",
       "2016-01-01 19:00:00   -0.062260\n",
       "2016-01-01 20:00:00   -0.062260\n",
       "2016-01-01 21:00:00   -0.062260\n",
       "2016-01-01 22:00:00   -0.062260\n",
       "2016-01-01 23:00:00   -0.062260\n",
       "2016-01-02 00:00:00   -0.062260\n",
       "2016-01-02 01:00:00   -0.812622\n",
       "2016-01-02 02:00:00   -0.812622\n",
       "2016-01-02 03:00:00   -0.812622\n",
       "2016-01-02 04:00:00   -0.812622\n",
       "2016-01-02 05:00:00   -0.812622\n",
       "                         ...   \n",
       "2016-12-29 19:00:00   -1.447418\n",
       "2016-12-29 20:00:00   -1.447418\n",
       "2016-12-29 21:00:00   -1.447418\n",
       "2016-12-29 22:00:00   -1.447418\n",
       "2016-12-29 23:00:00   -1.447418\n",
       "2016-12-30 00:00:00   -1.447418\n",
       "2016-12-30 01:00:00    0.373758\n",
       "2016-12-30 02:00:00    0.373758\n",
       "2016-12-30 03:00:00    0.373758\n",
       "2016-12-30 04:00:00    0.373758\n",
       "2016-12-30 05:00:00    0.373758\n",
       "2016-12-30 06:00:00    0.373758\n",
       "2016-12-30 07:00:00    0.373758\n",
       "2016-12-30 08:00:00    0.373758\n",
       "2016-12-30 09:00:00    0.373758\n",
       "2016-12-30 10:00:00    0.373758\n",
       "2016-12-30 11:00:00    0.373758\n",
       "2016-12-30 12:00:00    0.373758\n",
       "2016-12-30 13:00:00    0.373758\n",
       "2016-12-30 14:00:00    0.373758\n",
       "2016-12-30 15:00:00    0.373758\n",
       "2016-12-30 16:00:00    0.373758\n",
       "2016-12-30 17:00:00    0.373758\n",
       "2016-12-30 18:00:00    0.373758\n",
       "2016-12-30 19:00:00    0.373758\n",
       "2016-12-30 20:00:00    0.373758\n",
       "2016-12-30 21:00:00    0.373758\n",
       "2016-12-30 22:00:00    0.373758\n",
       "2016-12-30 23:00:00    0.373758\n",
       "2016-12-31 00:00:00    0.373758\n",
       "Freq: H, Length: 8761, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.resample('H').bfill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t_range = pd.date_range('2016-01-01', '2016-12-31', freq='H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00',\n",
       "               '2016-01-01 02:00:00', '2016-01-01 03:00:00',\n",
       "               '2016-01-01 04:00:00', '2016-01-01 05:00:00',\n",
       "               '2016-01-01 06:00:00', '2016-01-01 07:00:00',\n",
       "               '2016-01-01 08:00:00', '2016-01-01 09:00:00',\n",
       "               ...\n",
       "               '2016-12-30 15:00:00', '2016-12-30 16:00:00',\n",
       "               '2016-12-30 17:00:00', '2016-12-30 18:00:00',\n",
       "               '2016-12-30 19:00:00', '2016-12-30 20:00:00',\n",
       "               '2016-12-30 21:00:00', '2016-12-30 22:00:00',\n",
       "               '2016-12-30 23:00:00', '2016-12-31 00:00:00'],\n",
       "              dtype='datetime64[ns]', length=8761, freq='H')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stock_df = DataFrame(index=t_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_df['BABA'] = np.random.randint(80, 160, size=len(t_range))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_df['TENCENT'] = np.random.randint(30, 50, size=len(t_range))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BABA</th>\n",
       "      <th>TENCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>121</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 01:00:00</th>\n",
       "      <td>133</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 02:00:00</th>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 03:00:00</th>\n",
       "      <td>135</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 04:00:00</th>\n",
       "      <td>136</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BABA  TENCENT\n",
       "2016-01-01 00:00:00   121       38\n",
       "2016-01-01 01:00:00   133       33\n",
       "2016-01-01 02:00:00   121       34\n",
       "2016-01-01 03:00:00   135       46\n",
       "2016-01-01 04:00:00   136       40"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1108e3198>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aQfx876ne9NYNa/CHwa1DHuiCCdRz/Oj6ISUC23e3n8yD5xzvff/KH/ox4eYT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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcf7400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekly_df = DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekly_df['BABA'] = stock_df['BABA'].resample('W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekly_df['TENCENT'] = stock_df['TENCENT'].resample('W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BABA</th>\n",
       "      <th>TENCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>121.013889</td>\n",
       "      <td>39.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>117.791667</td>\n",
       "      <td>39.422619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>122.011905</td>\n",
       "      <td>40.220238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>122.714286</td>\n",
       "      <td>39.184524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>121.392857</td>\n",
       "      <td>39.601190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BABA    TENCENT\n",
       "2016-01-03  121.013889  39.041667\n",
       "2016-01-10  117.791667  39.422619\n",
       "2016-01-17  122.011905  40.220238\n",
       "2016-01-24  122.714286  39.184524\n",
       "2016-01-31  121.392857  39.601190"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1108e30f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZGVk17Ujp6em8//77XHDBBYeNT0tL4/LLL2fOnDlMnDgRgLlz55KbmwvAlVde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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ea36d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
